Regression Language Models for Code

• URL: http://arxiv.org/abs/2509.26476v1
• Date: Tue, 30 Sep 2025 16:25:23 GMT
• Title: Regression Language Models for Code
• Abstract summary: We show that a single unified Regression Language Model (RLM) can simultaneously predict directly from text.<n>We show that the RLM can obtain the highest average Kendall-Tau of 0.46 on five classic NAS design spaces.
• Score: 13.53022663049873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study code-to-metric regression: predicting numeric outcomes of code executions, a challenging task due to the open-ended nature of programming languages. While prior methods have resorted to heavy and domain-specific feature engineering, we show that a single unified Regression Language Model (RLM) can simultaneously predict directly from text, (i) the memory footprint of code across multiple high-level languages such as Python and C++, (ii) the latency of Triton GPU kernels, and (iii) the accuracy and speed of trained neural networks represented in ONNX. In particular, a relatively small 300M parameter RLM initialized from T5Gemma, obtains > 0.9 Spearman-rank on competitive programming submissions from APPS, and a single unified model achieves > 0.5 average Spearman-rank across 17 separate languages from CodeNet. Furthermore, the RLM can obtain the highest average Kendall-Tau of 0.46 on five classic NAS design spaces previously dominated by graph neural networks, and simultaneously predict architecture latencies on numerous hardware platforms.

Related papers

• Consensus-gated Multi-Agent Neural Architecture Search for Seismic Fault Segmentation [0.17188280334580194]
  Neural networks for seismic fault segmentation are often borrowed from computer vision and medical imaging domains.<n>Classical neural architecture search (NAS) is restricted to hand-crafted search spaces and large compute budgets.<n>We present an agentic NAS system in which a panel of three large language models (Claude, GPT-5.1, and Gemini2.5Pro) debates each candidate architecture to unanimous consensus.
  arXiv  Detail & Related papers  (2026-08-14T02:41:37Z)
• TernaryLM: Memory-Efficient Language Modeling via Native 1-Bit Quantization with Adaptive Layer-wise Scaling [0.39287497907611874]
  We present TernaryLM, a 132M parameter transformer architecture that employs native 1-bit ternary quantization -1, 0, +1 during training.<n>Our results suggest that native 1-bit training is a promising direction for efficient neural language models.
  arXiv  Detail & Related papers  (2026-02-07T05:35:17Z)
• NNGPT: Rethinking AutoML with Large Language Models [36.90850535125572]
  NNGPT is an open-source framework that turns a large language model (LLM) into a self-improving AutoML engine for neural network development.<n>It integrates within one unified workflow five synergistic LLM-based pipelines: zero-shot architecture synthesis, hyper parameter optimization, code-aware accuracy/early-stop prediction, and reinforcement learning.<n>The system has already generated over 5K validated models, proving NNGPT as an autonomous AutoML engine.
  arXiv  Detail & Related papers  (2025-11-25T14:10:44Z)
• Training Neural Networks as Recognizers of Formal Languages [87.06906286950438]
  We train and evaluate neural networks directly as binary classifiers of strings.<n>We provide results on a variety of languages across the Chomsky hierarchy for three neural architectures.<n>Our contributions will facilitate theoretically sound empirical testing of language recognition claims in future work.
  arXiv  Detail & Related papers  (2024-11-11T16:33:25Z)
• Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning [73.73967342609603]
  We introduce a predictor-corrector learning framework to minimize truncation errors. We also propose an exponential moving average-based coefficient learning method to strengthen our higher-order predictor. Our model surpasses a robust 3.8B DeepNet by an average of 2.9 SacreBLEU, using only 1/3 parameters.
  arXiv  Detail & Related papers  (2024-11-05T12:26:25Z)
• Large Language Models as Interpolated and Extrapolated Event Predictors [10.32127659470566]
  We investigate how large language models (LLMs) streamline the design of event prediction frameworks.<n>We propose LEAP, a unified framework that leverages large language models as event predictors.
  arXiv  Detail & Related papers  (2024-06-15T04:09:31Z)
• MPIrigen: MPI Code Generation through Domain-Specific Language Models [3.5352856644774806]
  This study first investigates the performance of state-of-the-art language models in generating MPI-based parallel programs. We introduce a dedicated downstream task of MPI-based program generation by fine-tuning MonoCoder on HPCorpusMPI. The success of this tailored solution underscores the importance of domain-specific fine-tuning in optimizing language models for parallel computing code generation.
  arXiv  Detail & Related papers  (2024-02-14T12:24:21Z)
• In-Context Language Learning: Architectures and Algorithms [73.93205821154605]
  We study ICL through the lens of a new family of model problems we term in context language learning (ICLL) We evaluate a diverse set of neural sequence models on regular ICLL tasks.
  arXiv  Detail & Related papers  (2024-01-23T18:59:21Z)
• Symbolic Regression on FPGAs for Fast Machine Learning Inference [2.0920303420933273]
  High-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) We introduce a novel end-to-end procedure that utilizes a machine learning technique called symbolic regression (SR) We show that our approach can approximate a 3-layer neural network using an inference model that achieves up to a 13-fold decrease in execution time, down to 5 ns, while still preserving more than 90% approximation accuracy.
  arXiv  Detail & Related papers  (2023-05-06T17:04:02Z)
• CodeGen2: Lessons for Training LLMs on Programming and Natural Languages [116.74407069443895]
  We unify encoder and decoder-based models into a single prefix-LM. For learning methods, we explore the claim of a "free lunch" hypothesis. For data distributions, the effect of a mixture distribution and multi-epoch training of programming and natural languages on model performance is explored.
  arXiv  Detail & Related papers  (2023-05-03T17:55:25Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• Model Fusion via Optimal Transport [64.13185244219353]
  We present a layer-wise model fusion algorithm for neural networks. We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
  arXiv  Detail & Related papers  (2019-10-12T22:07:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.